# coordinates are 36.17, -115.14
lv_full <- fread("data/Las_Vegas_Solar_Irradiation/LV.csv")
head(lv_full)
## Year Month Day Hour Minute Dew Point Surface Albedo Wind Speed
## 1: 2000 1 1 0 0 -4 0.169 2.2
## 2: 2000 1 1 0 30 -4 0.169 2.2
## 3: 2000 1 1 1 0 -3 0.169 2.2
## 4: 2000 1 1 1 30 -3 0.169 2.0
## 5: 2000 1 1 2 0 -2 0.169 1.9
## 6: 2000 1 1 2 30 -2 0.169 1.7
## Relative Humidity Temperature Pressure Wind Direction Precipitable Water
## 1: 55.3 6 940 267 1.03
## 2: 55.3 6 940 267 1.04
## 3: 59.0 6 940 274 1.05
## 4: 59.0 6 940 274 1.05
## 5: 61.4 6 940 278 1.06
## 6: 61.3 6 940 278 1.06
## Solar Zenith Angle GHI Cloud Type
## 1: 166 0 4
## 2: 164 0 8
## 3: 159 0 4
## 4: 154 0 4
## 5: 148 0 4
## 6: 142 0 1
## Classes 'data.table' and 'data.frame': 367920 obs. of 16 variables:
## $ Year : int 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
## $ Month : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Day : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Hour : int 0 0 1 1 2 2 3 3 4 4 ...
## $ Minute : int 0 30 0 30 0 30 0 30 0 30 ...
## $ Dew Point : num -4 -4 -3 -3 -2 -2 -2 -2 -2 -2 ...
## $ Surface Albedo : num 0.169 0.169 0.169 0.169 0.169 0.169 0.169 0.169 0.169 0.169 ...
## $ Wind Speed : num 2.2 2.2 2.2 2 1.9 1.7 1.5 1.4 1.4 1.3 ...
## $ Relative Humidity : num 55.3 55.3 59 59 61.4 ...
## $ Temperature : num 6 6 6 6 6 6 6 5 5 5 ...
## $ Pressure : int 940 940 940 940 940 940 940 940 940 940 ...
## $ Wind Direction : num 267 267 274 274 278 ...
## $ Precipitable Water: num 1.03 1.04 1.05 1.05 1.06 ...
## $ Solar Zenith Angle: num 166 164 159 154 148 ...
## $ GHI : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Cloud Type : int 4 8 4 4 4 1 4 4 1 4 ...
## - attr(*, ".internal.selfref")=<externalptr>
lv_full$Date <- make_datetime(year=lv_full$Year, month=lv_full$Month, day=lv_full$Day, hour=lv_full$Hour, min=lv_full$Minute)
lv_full %>% ggplot(aes(x=Date, y=Temperature)) + geom_line(color="blue")
plots <- NULL
# create a graph for each of the 20 years
temp <- lv_full %>%
# average by year-month
group_by(Year, Month) %>%
summarise(Temperature = mean(Temperature, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = Month, y = Temperature, color = factor(Year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average Temperature by Month", colour = "Year")
dew <- lv_full %>%
# average by year-month
group_by(Year, Month) %>%
summarise(`Dew Point` = mean(`Dew Point`, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = Month, y = `Dew Point`, color = factor(Year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average Dew Point by Month", colour = "Year")
humidity <- lv_full %>%
# average by year-month
group_by(Year, Month) %>%
summarise(`Relative Humidity` = mean(`Relative Humidity`, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = Month, y = `Relative Humidity`, color = factor(Year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average Relative Humidity by Month", colour = "Year")
albedo <- lv_full %>%
# average by year-month
group_by(Year, Month) %>%
summarise(`Surface Albedo` = mean(`Surface Albedo`, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = Month, y = `Surface Albedo`, color = factor(Year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average Surface Albedo by Month", colour = "Year")
wind <- lv_full %>%
# average by year-month
group_by(Year, Month) %>%
summarise(`Wind Speed` = mean(`Wind Speed`, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = Month, y = `Wind Speed`, color = factor(Year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average Wind Speed by Month", colour = "Year")
pressure <- lv_full %>%
# average by year-month
group_by(Year, Month) %>%
summarise(`Pressure` = mean(`Pressure`, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = Month, y = `Pressure`, color = factor(Year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average Pressure by Month", colour = "Year")
windD <- lv_full %>%
# average by year-month
group_by(Year, Month) %>%
summarise(`Wind Direction` = mean(`Wind Direction`, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = Month, y = `Wind Direction`, color = factor(Year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average Wind Direction by Month", colour = "Year")
precip <- lv_full %>%
# average by year-month
group_by(Year, Month) %>%
summarise(`Precipitable Water` = mean(`Precipitable Water`, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = Month, y = `Precipitable Water`, color = factor(Year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average Precipitable Water by Month", colour = "Year")
zenith <- lv_full %>% ggplot(aes(x=`Solar Zenith Angle`)) + geom_histogram(bins=360) + scale_x_continuous(breaks=seq(0,360,30)) + labs(title="Solar Zenith Angle Histogram")
ghi <- lv_full %>%
# average by year-month
group_by(Year, Month) %>%
summarise(`GHI` = mean(`GHI`, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = Month, y = `GHI`, color = factor(Year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average GHI by Month", colour = "Year")
cloud <- lv_full %>% ggplot(aes(x=`Cloud Type`)) + geom_histogram(bins=11) + scale_x_continuous(breaks=seq(0,10,1))+ labs(title="Cloud Type")
vsTemp <- lv_full %>%
group_by(Year, Month) %>%
summarise(`GHI` = mean(`GHI`, na.rm=TRUE), `Temperature` = mean(`Temperature`, na.rm=TRUE)) %>%
ggplot(aes(x=`Temperature`, y=GHI)) + geom_point()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
vsDew <- lv_full %>%
group_by(Year, Month) %>%
summarise(`GHI` = mean(`GHI`, na.rm=TRUE), `Dew Point` = mean(`Dew Point`, na.rm=TRUE)) %>%
ggplot(aes(x=`Dew Point`, y=GHI)) + geom_point()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
vsHumid <- lv_full %>%
group_by(Year, Month) %>%
summarise(`GHI` = mean(`GHI`, na.rm=TRUE), `Relative Humidity` = mean(`Relative Humidity`, na.rm=TRUE)) %>%
ggplot(aes(x=`Relative Humidity`, y=GHI)) + geom_point()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
vsAlbedo <- lv_full %>%
group_by(Year, Month) %>%
summarise(`GHI` = mean(`GHI`, na.rm=TRUE), `Surface Albedo` = mean(`Surface Albedo`, na.rm=TRUE)) %>%
ggplot(aes(x=`Surface Albedo`, y=GHI)) + geom_point()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
vsWindS <- lv_full %>%
group_by(Year, Month) %>%
summarise(`GHI` = mean(`GHI`, na.rm=TRUE), `Wind Speed` = mean(`Wind Speed`, na.rm=TRUE)) %>%
ggplot(aes(x=`Wind Speed`, y=GHI)) + geom_point()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
vsPressure <- lv_full %>%
group_by(Year, Month) %>%
summarise(`GHI` = mean(`GHI`, na.rm=TRUE), `Pressure` = mean(`Pressure`, na.rm=TRUE)) %>%
ggplot(aes(x=`Pressure`, y=GHI)) + geom_point()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
vsWindD <- lv_full %>%
group_by(Year, Month) %>%
summarise(`GHI` = mean(`GHI`, na.rm=TRUE), `Wind Direction` = mean(`Wind Direction`, na.rm=TRUE)) %>%
ggplot(aes(x=`Wind Direction`, y=GHI)) + geom_point()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
vsPrecip <- lv_full %>%
group_by(Year, Month) %>%
summarise(`GHI` = mean(`GHI`, na.rm=TRUE), `Precipitable Water` = mean(`Precipitable Water`, na.rm=TRUE)) %>%
ggplot(aes(x=`Precipitable Water`, y=GHI)) + geom_point()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
vsWindD <- lv_full %>%
group_by(Year, Month) %>%
summarise(`GHI` = mean(`GHI`, na.rm=TRUE), `Wind Direction` = mean(`Wind Direction`, na.rm=TRUE)) %>%
ggplot(aes(x=`Wind Direction`, y=GHI)) + geom_point()
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
vsZenith <- lv_full %>%
group_by(`Solar Zenith Angle`) %>%
summarise(average_ghi = mean(GHI, na.rm=TRUE)) %>%
ggplot(aes(x=`Solar Zenith Angle`, y=average_ghi)) + geom_point()
vsCloud <- lv_full %>%
group_by(`Cloud Type`) %>%
summarise(average_ghi = mean(GHI, na.rm=TRUE)) %>%
ggplot(aes(x=`Cloud Type`, y=average_ghi)) + geom_point()
plots <- list(temp, dew, humidity, albedo, wind, pressure, windD, precip, zenith, ghi, cloud)
plots
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
##
## [[7]]
##
## [[8]]
##
## [[9]]
##
## [[10]]
##
## [[11]]
plotsVs <- list(vsTemp, vsDew, vsHumid, vsAlbedo, vsWindS, vsPressure, vsWindD, vsPrecip, vsZenith, vsCloud)
plotsVs
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
##
## [[7]]
##
## [[8]]
##
## [[9]]
##
## [[10]]
## Year Month Day Hour Minute
## Min. :2000 Min. : 1.00 Min. : 1.0 Min. : 0.00 Min. : 0
## 1st Qu.:2005 1st Qu.: 4.00 1st Qu.: 8.0 1st Qu.: 5.75 1st Qu.: 0
## Median :2010 Median : 7.00 Median :16.0 Median :11.50 Median :15
## Mean :2010 Mean : 6.53 Mean :15.7 Mean :11.50 Mean :15
## 3rd Qu.:2015 3rd Qu.:10.00 3rd Qu.:23.0 3rd Qu.:17.25 3rd Qu.:30
## Max. :2020 Max. :12.00 Max. :31.0 Max. :23.00 Max. :30
## Dew Point Surface Albedo Wind Speed Relative Humidity
## Min. :-27.00 Min. :0.146 Min. : 0.00 Min. : 2.2
## 1st Qu.: -5.00 1st Qu.:0.165 1st Qu.: 1.30 1st Qu.: 17.2
## Median : -1.00 Median :0.173 Median : 2.00 Median : 29.1
## Mean : -0.93 Mean :0.176 Mean : 2.58 Mean : 34.2
## 3rd Qu.: 3.00 3rd Qu.:0.184 3rd Qu.: 3.50 3rd Qu.: 46.5
## Max. : 23.00 Max. :0.870 Max. :13.40 Max. :100.0
## Temperature Pressure Wind Direction Precipitable Water
## Min. :-5.0 Min. :910 Min. : 0 Min. :0.10
## 1st Qu.:11.0 1st Qu.:940 1st Qu.: 93 1st Qu.:0.77
## Median :19.0 Median :940 Median :209 Median :1.10
## Mean :19.9 Mean :940 Mean :186 Mean :1.36
## 3rd Qu.:28.0 3rd Qu.:940 3rd Qu.:252 3rd Qu.:1.64
## Max. :47.0 Max. :960 Max. :360 Max. :6.80
## Solar Zenith Angle GHI Cloud Type
## Min. : 13.0 Min. : 0 Min. : 0.00
## 1st Qu.: 59.2 1st Qu.: 0 1st Qu.: 0.00
## Median : 89.8 Median : 0 Median : 0.00
## Mean : 89.7 Mean : 234 Mean : 1.92
## 3rd Qu.:120.4 3rd Qu.: 460 3rd Qu.: 4.00
## Max. :167.1 Max. :1084 Max. :10.00
## Date
## Min. :2000-01-01 00:00:00.00
## 1st Qu.:2005-04-02 05:52:30.00
## Median :2010-07-02 11:45:00.00
## Mean :2010-07-02 14:04:12.55
## 3rd Qu.:2015-10-01 17:37:30.00
## Max. :2020-12-31 23:30:00.00
#names(lv_full)
lv_full$Zenith_Bins <- cut(lv_full$`Solar Zenith Angle`, breaks = seq(0, 180, by = 10))
# Plotting irradiance vs zenith angle
# The gray dots are outliers of solar irradiance, likely caused by clouds
ggplot(data = lv_full, aes(x = Zenith_Bins, y = GHI)) +
geom_boxplot(outlier.color = "gray", size = 0.5) +
labs(x = "Solar Zenith Angle (degrees)", y = "Global Horizontal Irradiance (W/m²)",
title = "Distribution of GHI for Different Solar Zenith Angle (Grouped by bins of 10 degrees)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# plotting solar zenith against time of day
ggplot(data = lv_full, aes(x = Hour, y = `Solar Zenith Angle`, group = interaction(Month, Day), color = factor(Month))) +
geom_line() +
labs(x = "Time of Day (Hour)", y = "Solar Zenith Angle (degrees)",
title = "Solar Zenith Angle by Time of Day (Spaghetti Plot)") +
scale_color_discrete(name = "Month", labels = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")) +
theme_minimal() +
theme(legend.position = "top", axis.text.x = element_text(angle = 45, hjust = 1))
# shows the difference between solstices, and their effect on solar zenith angle
filtered_data <- lv_full %>%
filter(Month %in% c(6, 12))
ggplot(data = filtered_data, aes(x = Hour, y = `Solar Zenith Angle`, group = interaction(Month, Day), color = factor(Month))) +
geom_line() +
labs(x = "Time of Day (Hour)", y = "Solar Zenith Angle (degrees)",
title = "Solar Zenith Angle by Time of Day (June and December Only)") +
scale_color_discrete(name = "Month", labels = c("June", "December")) +
theme_minimal() +
theme(legend.position = "top", axis.text.x = element_text(angle = 45, hjust = 1))
#plotting how solar irradiance is affected by time of month
lv_full$Month <- factor(lv_full$Month, levels = 1:12, labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))
agg_data <- lv_full %>%
group_by(Month, Hour) %>%
summarize(Avg_GHI = mean(GHI))
## `summarise()` has grouped output by 'Month'. You can override using the
## `.groups` argument.
ggplot(data = lv_full, aes(x = Hour, y = GHI, group = interaction(Month, Hour), color = Month)) +
geom_line(size = 1.5, alpha = 0.7) + # Set the size to 1.5 (adjust as needed)
labs(x = "Hour of the Day", y = "Average Global Horizontal Irradiance (W/m²)",
title = "Average Solar Irradiance by Hour for Each Month (Spaghetti Plot)") +
scale_x_continuous(breaks = seq(0, 23, by = 1)) +
theme_minimal() +
theme(legend.position = "top", axis.text.x = element_text(angle = 45, hjust = 1))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# num_unique_clouds <- lv_full %>%
# distinct(Cloud_Type) %>%
# nrow()
lv_full <- lv_full %>%
rename(Cloud_Type = `Cloud Type`)
lv_full$Cloud_Type <- factor (lv_full$Cloud_Type)
# Effect of clouds on GHI
ggplot(data = lv_full, aes(x = Cloud_Type, y = GHI)) +
stat_summary(data = subset(lv_full, `Solar Zenith Angle` >= 0 & `Solar Zenith Angle` <= 80),
fun = "mean", geom = "bar", fill = "skyblue", color = "black") +
labs(x = "Cloud_Type", y = "Mean Global Horizontal Irradiance (W/m²)",
title = "Effect of Cloud Type on Irradiance (Solar Zenith: 0-80 degrees)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
## [1] "Year" "Month" "Day"
## [4] "Hour" "Minute" "Dew Point"
## [7] "Surface Albedo" "Wind Speed" "Relative Humidity"
## [10] "Temperature" "Pressure" "Wind Direction"
## [13] "Precipitable Water" "Solar Zenith Angle" "GHI"
## [16] "Cloud_Type" "Date" "Zenith_Bins"
# temperature vs irradiance boxplot
ggplot(data = subset(lv_full, `Solar Zenith Angle` >= 0 & `Solar Zenith Angle` <= 80),
aes(x = cut(Temperature, breaks = seq(min(Temperature), max(Temperature) + 2, by = 2)), y = GHI)) +
geom_boxplot() +
labs(x = "Temperature (°C)", y = "Global Horizontal Irradiance (W/m²)",
title = "Temperature vs. Global Horizontal Irradiance (Solar Zenith: 0-80 degrees)") +
theme_minimal()
#Surface Albedo vs Irradiance for sunlight hours
filtered_data <- lv_full %>%
filter(`Solar Zenith Angle` >= 0 & `Solar Zenith Angle` <= 80,
`Surface Albedo` >= 0.10 & `Surface Albedo` <= 0.25)
filtered_data <- filtered_data %>%
mutate(Albedo_Bin = cut(`Surface Albedo`, breaks = seq(0.10, 0.25, by = 0.005)),)
ggplot(data = filtered_data, aes(x = Albedo_Bin, y = GHI)) +
geom_boxplot(outlier.color = "gray", size = 0.5) +
labs(x = "Surface Albedo", y = "Global Horizontal Irradiance (W/m²)",
title = "Distribution of GHI for Different Surface Albedo (Grouped by bins of 0.005)\n(Zenith: 0-80 degrees)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
#Relative humidity vs Solar Irradiance for zentih 0-80
filtered_data <- filtered_data %>%
mutate(RelHumidity_Bin = cut(`Relative Humidity`, breaks = seq(0, 100, by = 2.5)))
ggplot(data = filtered_data, aes(x = RelHumidity_Bin, y = GHI)) +
geom_boxplot(fill = "skyblue", color = "black") +
labs(x = "Relative Humidity (%)", y = "Global Horizontal Irradiance (W/m²)",
title = "GHI vs. Relative Humidity (Zenith: 0-80 degrees, Bin Size: 2.5%)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
#Precipitable Water vs GHI (for hours during sunlight)
filtered_data <- filtered_data %>%
mutate(PrecipitableWater_Bin = cut(`Precipitable Water`, breaks = seq(min(`Precipitable Water`), max(`Precipitable Water`), by = 0.15)))
ggplot(data = filtered_data, aes(x = PrecipitableWater_Bin, y = GHI)) +
geom_boxplot(fill = "skyblue", color = "black") +
labs(x = "Precipitable Water (Bin Size: 0.05)", y = "Global Horizontal Irradiance (W/m²)",
title = "GHI vs. Precipitable Water (Zenith: 0-80 degrees)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
